A Canonical Data Transformation for Achieving Inter- and Within-Group Fairness

Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorit...

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Veröffentlicht in:	IEEE transactions on information forensics and security 2024, Vol.19, p.7449-7464
Hauptverfasser:	Lazri, Zachary McBride, Brugere, Ivan, Tian, Xin, Dachman-Soled, Dana, Polychroniadou, Antigoni, Dervovic, Danial, Wu, Min
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial Intelligence (AI) canonical domain Computational geometry Data models data preprocessing Decision making fairness in AI/ML inter-group fairness Machine learning machine learning (ML) Testing Training Vectors within-group fairness
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